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A Comparison of Error Rates for EVA, Residual Income, GAAP-Earnings, and Other Metrics Using a Long-Window Valuation Approach

Dublin City University; UNSW Australia Business School, School of Banking and Finance; Financial Research Network (FIRN)

Abstract:

Predictability and variability are two measures commonly used in the empirical literature to gauge the quality of earnings and hence, decision usefulness to investors. We adopt both measures to investigate empirically the relative quality of Stern Stewart's measure of economic value added (EVA) compared to GAAP earnings, residual income, cash flows and other mandated metrics in the US and UK. We proxy for accounting quality by applying a long-window methodology to obtain hindsight valuation errors based on the difference between ex ante market value and discounted ex post metrics. Decision usefulness, in terms of ease of forecasting, is proxied by differences in valuation errors between the benchmark and alternative accounting methods. Contrary to the Biddle, Bowen and Wallace (1997) finding that mandated earnings were superior to EVA and residual income, we find that EVA and other residual income metrics consistently give rise to lower average valuation errors and thus have higher predictability across a variety of windows and terminal dates. Further, on the basis of our second measure of accounting quality, the variability of valuation errors, EVA performs best in the US and third in the UK. The results strongly indicate that differences between residual income constructs, including EVA, are generally small but that earnings quality will be improved by recognition of a cost of equity capital in measuring reported income.